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Medium- to long-term nickel price forecasting using LSTM and GRU networks

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  • Ozdemir, Ali Can
  • Buluş, Kurtuluş
  • Zor, Kasım

Abstract

Recently, nickel is a critical metal for manufacturing stainless steel, rechargeable electric vehicle batteries, and alloys utilized in the state-of-the-art technologies. The use of more environmentally friendly electric vehicles has become widespread and brought tackling climate change to forefront, especially for reducing greenhouse gas emissions. Therefore, the demand for rechargeable batteries that power electric vehicles and the need for the nickel in the production of these batteries will increase as well. In addition to those, nickel prices significantly impact mine investment decisions, mine planning, economic development of nickel companies, and countries that depend on nickel resources. However, there is uncertainty about how the nickel price will trend in the future, and the solution to this problem attracts the attention of researchers. For forecasting nickel price, this paper proposes recurrent neural networks-based on long short-term memory (LSTM) and gated recurrent unit (GRU) networks, classified as deep learning algorithms. Mean absolute percentage error (MAPE) was used as the performance measure to compute the accuracy of the proposed techniques. As a result, it has been determined that the LSTM and GRU networks are very useful and successful in forecasting the nickel price variations owing to having average MAPE values of 7.060% and 6.986%, respectively. Furthermore, it has been observed that GRU networks surpassed the LSTM networks by 33% in terms of average computational time.

Suggested Citation

  • Ozdemir, Ali Can & Buluş, Kurtuluş & Zor, Kasım, 2022. "Medium- to long-term nickel price forecasting using LSTM and GRU networks," Resources Policy, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:jrpoli:v:78:y:2022:i:c:s0301420722003506
    DOI: 10.1016/j.resourpol.2022.102906
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    as
    1. Liu, Kailei & Cheng, Jinhua & Yi, Jiahui, 2022. "Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform," Resources Policy, Elsevier, vol. 75(C).
    2. Behmiri, Niaz Bashiri & Manera, Matteo, 2015. "The role of outliers and oil price shocks on volatility of metal prices," Resources Policy, Elsevier, vol. 46(P2), pages 139-150.
    3. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    4. Abdel Sabour, S. A., 2002. "Mine size optimization using marginal analysis," Resources Policy, Elsevier, vol. 28(3-4), pages 145-151.
    5. David L. McCollum & Wenji Zhou & Christoph Bertram & Harmen-Sytze Boer & Valentina Bosetti & Sebastian Busch & Jacques Després & Laurent Drouet & Johannes Emmerling & Marianne Fay & Oliver Fricko & Sh, 2018. "Author Correction: Energy investment needs for fulfilling the Paris Agreement and achieving the Sustainable Development Goals," Nature Energy, Nature, vol. 3(8), pages 699-699, August.
    6. Bilin Shao & Maolin Li & Yu Zhao & Genqing Bian, 2019. "Nickel Price Forecast Based on the LSTM Neural Network Optimized by the Improved PSO Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-15, September.
    7. He, Kaijian & Lu, Xingjing & Zou, Yingchao & Keung Lai, Kin, 2015. "Forecasting metal prices with a curvelet based multiscale methodology," Resources Policy, Elsevier, vol. 45(C), pages 144-150.
    8. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    9. Schmidt, Tobias & Buchert, Matthias & Schebek, Liselotte, 2016. "Investigation of the primary production routes of nickel and cobalt products used for Li-ion batteries," Resources, Conservation & Recycling, Elsevier, vol. 112(C), pages 107-122.
    10. Panas, E., 2001. "Long memory and chaotic models of prices on the London Metal Exchange," Resources Policy, Elsevier, vol. 27(4), pages 235-246, December.
    11. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    12. Bhatia, Vaneet & Das, Debojyoti & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Hasim, Haslifah M., 2018. "Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 55(C), pages 244-252.
    13. Fernandez, Viviana, 2018. "Price and income elasticity of demand for mineral commodities," Resources Policy, Elsevier, vol. 59(C), pages 160-183.
    14. Karmellos, M. & Kosmadakis, V. & Dimas, P. & Tsakanikas, A. & Fylaktos, N. & Taliotis, C. & Zachariadis, T., 2021. "A decomposition and decoupling analysis of carbon dioxide emissions from electricity generation: Evidence from the EU-27 and the UK," Energy, Elsevier, vol. 231(C).
    15. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    16. David L. McCollum & Wenji Zhou & Christoph Bertram & Harmen-Sytze Boer & Valentina Bosetti & Sebastian Busch & Jacques Després & Laurent Drouet & Johannes Emmerling & Marianne Fay & Oliver Fricko & Sh, 2018. "Energy investment needs for fulfilling the Paris Agreement and achieving the Sustainable Development Goals," Nature Energy, Nature, vol. 3(7), pages 589-599, July.
    17. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    18. Madziwa, Lawrence & Pillalamarry, Mallikarjun & Chatterjee, Snehamoy, 2022. "Gold price forecasting using multivariate stochastic model," Resources Policy, Elsevier, vol. 76(C).
    19. Eckelman, Matthew J., 2010. "Facility-level energy and greenhouse gas life-cycle assessment of the global nickel industry," Resources, Conservation & Recycling, Elsevier, vol. 54(4), pages 256-266.
    20. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
    21. Changqing Cheng & Akkarapol Sa-Ngasoongsong & Omer Beyca & Trung Le & Hui Yang & Zhenyu (James) Kong & Satish T.S. Bukkapatnam, 2015. "Time series forecasting for nonlinear and non-stationary processes: a review and comparative study," IISE Transactions, Taylor & Francis Journals, vol. 47(10), pages 1053-1071, October.
    22. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.
    23. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    24. Khoshalan, Hasel Amini & Shakeri, Jamshid & Najmoddini, Iraj & Asadizadeh, Mostafa, 2021. "Forecasting copper price by application of robust artificial intelligence techniques," Resources Policy, Elsevier, vol. 73(C).
    25. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    26. A. Desgagné & P. Lafaye de Micheaux, 2018. "A powerful and interpretable alternative to the Jarque–Bera test of normality based on 2nd-power skewness and kurtosis, using the Rao's score test on the APD family," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(13), pages 2307-2327, October.
    27. Fu, Rao & Jin, Gui & Chen, Jinyue & Ye, Yuyao, 2021. "The effects of poverty alleviation investment on carbon emissions in China based on the multiregional input–output model," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    28. Hatayama, Hiroki & Tahara, Kiyotaka, 2018. "Adopting an objective approach to criticality assessment: Learning from the past," Resources Policy, Elsevier, vol. 55(C), pages 96-102.
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    2. László Vancsura & Tibor Tatay & Tibor Bareith, 2023. "Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times," Risks, MDPI, vol. 11(2), pages 1-16, January.
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    4. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.

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